A comparative study of various meta-heuristic techniques applied to the multilevel thresholding problem

被引：143
作者：
Hammouche, Kamal [2 ]
Diaf, Moussa [2 ]
Siarry, Patrick [1 ]
机构：
[1] Univ Paris 12, LiSSi, EA 3956, F-94010 Creteil, France
[2] Univ Mouloud Mammeri, Dept Automat, Tizi Ouzou, Algeria
来源：
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | 2010年 / 23卷 / 05期
关键词：
Multilevel thresholding; Image segmentation; Genetic algorithm; Particle swarm optimization; Differential evolution; Ant colony optimization; Simulated annealing; Tabu search; PARTICLE SWARM OPTIMIZATION; IMAGE SEGMENTATION; DIFFERENTIAL EVOLUTION; ALGORITHM; ENTROPY; SCHEME;
D O I：
10.1016/j.engappai.2009.09.011
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The multilevel thresholding problem is often treated as a problem of optimization of an objective function. This paper presents both adaptation and comparison of six meta-heuristic techniques to solve the multilevel thresholding problem: a genetic algorithm, particle swarm optimization, differential evolution, ant colony, simulated annealing and tabu search. Experiments results show that the genetic algorithm, the particle swarm optimization and the differential evolution are much better in terms of precision, robustness and time convergence than the ant colony, simulated annealing and tabu search. Among the first three algorithms, the differential evolution is the most efficient with respect to the quality of the solution and the particle swarm optimization converges the most quickly. (C) 2009 Elsevier Ltd. All rights reserved.
引用
收藏
页码：676 / 688
页数：13
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